{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ff50cf71-9ae7-4b8d-8e59-2f12ecf5be7c",
   "metadata": {},
   "source": [
    "## **模式识别综述2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d5ab1375-492d-4be1-bbe5-5b32767df582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# 生成20个随机点，1个待测随机点，20个标签\n",
    "X = np.random.randint(0,255,(20,2)) \n",
    "pt = np.random.randint(0,255,2) \n",
    "y = np.random.randint(0,2,(20,1))\n",
    "\n",
    "# 2. 训练模型\n",
    "clf = KNeighborsClassifier(n_neighbors=1)  # 只考虑一个最近邻的KNN模型\n",
    "clf.fit(X, y.ravel())\n",
    "\n",
    "# 3. 预测\n",
    "## 基础预测：预测标签\n",
    "ptList = [pt] # 待预测的列表中只有一个pt\n",
    "predictionList = clf.predict(ptList) # 获得预测的结果列表\n",
    "pt_class=predictionList[0] #pt点的类别是结果列表中的第一个元素\n",
    "\n",
    "## 高级预测：得到最近点的坐标\n",
    "distance, index = clf.kneighbors(ptList) # 获得所有待预测点列表对应的每一个待测点的最近邻列表\n",
    "# distance 这个列表中保存测试点与已有所有点的距离，从近到远排列\n",
    "# index 这个列表中保存按距离排序的所有点的索引\n",
    "NN = X[index[0, 0]]\n",
    "\n",
    "X=np.concatenate((X,y),axis=1) \n",
    "pt=np.concatenate((pt,[pt_class]))\n",
    "\n",
    "color=np.where(X[:,2]==1,'blue','green')#满足条件则返回blue,不满足返回green\n",
    "\n",
    "# 画图\n",
    "fg, ax = plt.subplots(figsize=(15,7))\n",
    "ax.grid()\n",
    "ax.plot(pt[0], pt[1], 'rx')  # rx->red \"x\" mark\n",
    "ax.scatter(X[:, 0], X[:, 1], c=color, s=50)#绘制散点\n",
    "ax.scatter(pt[0],pt[1],c='red',s=100)\n",
    "ax.plot([pt[0], NN[0]], [pt[1], NN[1]], 'r--')\n",
    "\n",
    "# 给所有的点加坐标标签\n",
    "for x,y,i in X:\n",
    "    s=f'({x},{y}) {i}'\n",
    "    ax.text(x,y,s,size=12)\n",
    "for x,y,i in [pt]:\n",
    "    s=f'({x},{y}) {i}'\n",
    "    ax.text(x,y,s,size=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4c151bf6-0f4e-4d3c-96c8-79d5dc75ed46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from numpy.random import randint \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "\n",
    "len=20 # 指定测试点个数\n",
    "X=randint(0,255,size=(len,2)) # 生成随机训练数据点20个测试点\n",
    "y=randint(0,2,size=(len))     # 生成20个随机训练数据点对应类别\n",
    "\n",
    "pt=randint(0,255,2) # 生成1个随机测试点\n",
    "########################################################\n",
    "# 开始模式识别\n",
    "########################################################\n",
    "clf=KNeighborsClassifier(n_neighbors=1) # 1.创建模型\n",
    "clf.fit(X,y) # 2.拟合数据\n",
    "\n",
    "prediction = clf.predict([pt]) # 用模型预测该点的类别\n",
    "_, index = clf.kneighbors([pt]) # 用模型获得该点的所有邻居的排序\n",
    "NN = X[index[0, 0]] # 获得最近邻NN\n",
    "# 第一个0 --> 对应需要判断的列表中的第几个点，只有一个pt，所以只有0\n",
    "# 第二个0 --> pt的最近邻那个点的索引值，如果是1就指的是下一个邻居\n",
    "\n",
    "########################################################\n",
    "# 开始作图\n",
    "########################################################\n",
    "fg,ax=plt.subplots(figsize=(20,7))\n",
    "ax.grid()\n",
    "ax.scatter(X[:,0],X[:,1],c=y,s=100)  # 画出所有的随机点，不同的类别颜色不同\n",
    "ax.scatter(pt[0],pt[1],c='red',s=200) # 画出大红色测试点\n",
    "\n",
    "# 画出所有随机点坐标的标签\n",
    "for i in range(len):\n",
    "    pt_str=\"({0},{1}) {2}\".format(X[i,0],X[i,1],y[i])\n",
    "    ax.text(X[i,0],X[i,1]+5,pt_str, color='blue',size=16)\n",
    "    \n",
    "# 画出测试点的标签\n",
    "pt_str=\"({0},{1}) {2}\".format(pt[0],pt[1],prediction[0])\n",
    "ax.text(pt[0],pt[1],pt_str, color='blue',size=16)\n",
    "\n",
    "# 在测试点与最近邻之间画线\n",
    "ax.plot([pt[0],NN[0]],[pt[1],NN[1]],'r--')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48c57b40-4bd5-4efd-baff-720c5621ecb3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
